Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Fa Zhu, Xingchi Chen, Shuo Chen, Wei Zheng, Weidu Ye
Summary: As a classical ordinal regression model, support vector ordinal regression (SVOR) finds parallel discriminant hyperplanes to maximize the minimal margins between different ranks. However, SVOR only considers minor patterns near the margin hyperplanes and ignores the contributions of other patterns. To address this issue, this paper proposes relative margin induced support vector ordinal regression (RMSVOR) models, which depict the margin between a pattern and a discriminant hyperplane based on relative margin information. Experimental results on various datasets show that RMSVOR outperforms previous ordinal regression models and canonical multi-class classification models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Business, Finance
Christina E. Barmier, Yannik Bofinger, Bjoern Rock
Summary: This study examines the relationship between corporate social responsibility and credit risk for U.S. and European firms from 2003 to 2018. The findings indicate that only the environmental aspect is negatively associated with credit risk for U.S. firms, while both environmental and social aspects are negatively related to credit risk for European firms. Surprisingly, credit ratings do not reflect the same contemporaneous relationship with corporate social responsibility. The robustness of the results is confirmed using different estimation methods.
FINANCE RESEARCH LETTERS
(2022)
Article
Computer Science, Information Systems
Fa Zhu, Xingchi Chen, Xizhan Gao, Weidu Ye, Hai Zhao, Athanasios V. Vasilakos
Summary: This paper proposes a method called Constraint-weighted Support Vector Ordinal Regression (CWSVOR) to address the problem of constraint noises in ordinal regression. By introducing a constraint weight vector to control the influence of constraints on parallel hyperplanes, CWSVOR aims to mitigate the detrimental effects of constraint noises and shows superior performance on training sets corrupted by noises.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
M. A. Ganaie, M. Tanveer, Alzheimer's Disease Neuroimaging Initiative
Summary: This paper introduces a novel fuzzy least squares projection twin support vector machines for class imbalance learning, which outperforms baseline models in experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Business, Finance
Nilesh B. Sah, Deepak G. More
Summary: Firms with dual class structure may have poor corporate governance and tend to use strategies to avoid external monitoring, such as reducing trade credit usage. Their behavior may be driven by a dislike of supplier oversight. Our findings are particularly significant for non-manufacturing firms and dual class firms with high bargaining power. We contribute to the literature by examining the trade credit policies of dual class firms and introducing an additional metric to assess their operational transparency and monitoring preferences.
FINANCE RESEARCH LETTERS
(2022)
Article
Automation & Control Systems
Jingxuan Pang, Xiaokun Pu, Chunguang Li
Summary: Anomaly detection plays a crucial role in industry for maintaining system safety and ensuring product quality. This article introduces a hybrid algorithm, VQ-OCSVM, which combines vector quantization and OCSVM to address the challenges faced by OCSVM in kernel parameter selection and handling complex data distributions. The proposed method effectively bypasses the kernel parameter selection problem and integrates generative and discriminative learning for better generalization capacity. Experimental results demonstrate the effectiveness and advantages of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls
Summary: Bivariate causal discovery is the task of inferring the causal relationship between two random variables from observational data. This paper proposes an ensemble algorithm that combines classical and data-driven methods, achieving superior performance on various synthetic and real-world problems.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Engineering, Electrical & Electronic
Dunbiao Niu, Chengjing Wang, Peipei Tang, Qingsong Wang, Enbin Song
Summary: This paper proposes a highly efficient sparse semismooth Newton (SsN) based augmented Lagrangian (AL) method for solving large-scale SVMs. The method utilizes the piecewise linear-quadratic structure of the problem and the sparse structure of the generalized Jacobian to achieve accurate and efficient solutions. Numerical experiments demonstrate that the proposed algorithm outperforms the current state-of-the-art solvers.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Imara Mohamed Nazar, Mustafa Aksoy
Summary: Radio frequency interference (RFI) poses a serious threat to accurate estimation of geophysical parameters via passive microwave remote sensing. This article presents a novel RFI detection algorithm that relies on information extracted from RFI-free radiometer measurements, using a one-class algorithm. The algorithm transforms raw radiometer measurements into a feature-based representation, selects the most discriminant features for interference detection, and computes optimal decision boundaries via support vector machines (SVM) using RFI-free measurements. Performance evaluation shows that the novel algorithm successfully detects RFI even at low interference-to-noise ratios.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Statistics & Probability
Jakob Raymaekers, Peter J. Rousseeuw, Mia Hubert
Summary: Classification is a crucial tool in statistics and machine learning that assigns objects to different classes for prediction. Label bias may occur when objects are predicted to belong to a class different from their given labels, raising concerns about mislabeling. The visualization of classification results through class mapping helps provide insights into the data.
Article
Physics, Multidisciplinary
Huan Liu, Jiankai Tu, Chunguang Li
Summary: This paper proposes a distributed SVOR algorithm to solve ordinal regression problems in distributed environments. Theoretical analysis and experimental results demonstrate that the proposed method can achieve good performance in scenarios where privacy protection or centralized data processing is not feasible.
Article
Engineering, Industrial
Kyoung-jae Kim, Hyunchul Ahn
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2017)
Article
Green & Sustainable Science & Technology
Suk-Joo Lee, Cheolhwi Ahn, Kelly Minjung Song, Hyunchul Ahn
Article
Computer Science, Information Systems
Hyunchul Ahn, Seongjin Kim, Jae Kyeong Kim
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
(2014)
Article
Green & Sustainable Science & Technology
Kyoung-jae Kim, Kichun Lee, Hyunchul Ahn
Article
Green & Sustainable Science & Technology
Kee-Young Kwahk, Sung-Byung Yang, Hyunchul Ahn
Article
Green & Sustainable Science & Technology
Byungchan Ahn, Hyunchul Ahn
Article
Computer Science, Artificial Intelligence
Jae-Seung Shim, Yunju Lee, Hyunchul Ahn
Summary: This study introduces a new method for fake news detection using the composition pattern of web links as a source of information and vectorizing it through link2vec technology. Experimental results demonstrate that the link2vec-based model outperforms traditional text-based models in both language independence and detection effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Woong Park, Hyunchul Ahn
Summary: This research presents a method for sustaining a firm's business by managing the heterogeneity of churn customers and analyzing their impact on customer behavior. The study finds that customer churn heterogeneity significantly affects customers' second-lifetime behavior and demonstrates how firms can maintain loyalty through customer regaining initiatives.
Article
Information Science & Library Science
Kee-Young Kwahk, Hyunchul Ahn, Young U. Ryu
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2018)
Article
Computer Science, Interdisciplinary Applications
Mehrbakhsh Nilashi, Karamollah Bagherifard, Mohsen Rahmani, Vahid Rafe
COMPUTERS & INDUSTRIAL ENGINEERING
(2017)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hyoung-Yong Lee, Hyunchul Ahn, Heung Kee Kim, Jongwon Lee
2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT, ITQM 2014
(2014)
Proceedings Paper
Computer Science, Information Systems
Namgyu Kim, William Wong Xiu Shun, Jieun Kim, Kee-Young Kwahk, Seungryul Jeong, Hyunchul Ahn
2014 IEEE INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS)
(2014)
Proceedings Paper
Health Care Sciences & Services
Seung Hee Ho, Hyunchul Ahn, Na Young Kim, So Yeon Yu, Ye Soon Kim, Jong Wook Won, Han Joon Kim, Sung You Cho
MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2
(2013)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)